Statistical language modeling is essential to a number of word prediction and word recognition implementations. In many cases, such modeling includes the use of one or more language models in pattern analysis, evaluation of text hypotheses, and selection of predicted results. One commonly used type of language model is an n-gram model, which conveys the probability of occurrences based on all possible strings of n words as extracted from a training database. With n-gram models, however, it is difficult to gather enough data such that all possible occurrences of n-word strings are represented with reasonable frequency. Recurrent neural networks are another form of language model often used in statistical language modeling, which estimate long-distance interword dependencies. Compared to n-gram models, recurrent neural networks are capable of providing robust word estimation, yet require more complex computation.